Job Overview
Location
US- remote
Job Type
Full-time
Category
Data Science
Date Posted
June 14, 2026
Full Job Description
đź“‹ Description
- • Prototype and train learning-based models using a data-centric approach, applying automated feature engineering, active learning, and fine-tuning on curated datasets.
- • Design, develop, and maintain efficient data and feature extraction pipelines to support ML engineers in accessing high-quality data for model training.
- • Design an auto-labeling system using an ensemble of models that reason from multimodal data for use cases including image semantic labeling with vision-grounded models, intent prediction, and path prediction ground truth.
- • Perform complex data extraction, transformation, and loading (ETL) processes to ensure data is clean, accessible, and well-documented.
- • Write and optimize high-quality SQL queries for data analysis and ingestion from structured, semi-structured, and unstructured data sources.
- • Partner with data infrastructure and ML engineers to ensure seamless integration of data pipelines and machine learning workflows.
- • Produce high-quality, maintainable code and actively participate in peer code reviews to share knowledge and uphold team standards.
- • Utilize machine-learning frameworks including TensorFlow and PyTorch to develop and iterate on models.
- • Work with multimodal data including images, point clouds, and time-series in the context of robotic delivery systems.
- • Leverage cloud platforms such as AWS, GCP, or Azure to deploy and scale data and ML infrastructure.
- • Apply containerization and workflow tools including Docker, Kubernetes, or Airflow to automate and manage ML pipelines.
- • Collaborate with cross-functional engineering teams to integrate data systems and machine learning workflows into production environments.
- • Program in Python to build scalable data pipelines and implement ETL workflows.
- • Work with relational databases including Postgres, Redshift, or SQL Server to support data ingestion and analysis.
- • Contribute to the development of robotic delivery systems by enabling machine learning models that improve sidewalk robot navigation, decision-making, and user experience.
- • Support the expansion of Serve Robotics’ commercial delivery operations in cities including Los Angeles, Miami, Dallas, Atlanta, and Chicago through data-driven model improvements.
🎯 Requirements
- • Bachelor’s Degree or U.S. equivalent in Computer Science, Data Science, or a related field
- • 5 years of professional experience as a Data Scientist, Machine Learning Engineer, Data Engineer, or related role performing software engineering and machine learning
- • 5 years of professional experience utilizing SQL to write and optimize complex queries for extraction, analysis, and ingestion of structured, semi-structured, and unstructured data
- • 5 years of professional experience utilizing machine-learning frameworks including TensorFlow and PyTorch
- • 5 years of professional experience designing and developing data and feature extraction pipelines for multimodal data (images, point clouds, time-series)
- • 5 years of professional experience training and prototyping machine-learning models using data-centric techniques (automated feature engineering, active learning, fine-tuning)
🏖️ Benefits
- • Salary range of $194,834 - $218,284 per year
- • 100% telecommuting from anywhere in the U.S.
- • Opportunity to work on real-world robotic delivery systems deployed in major U.S. cities
- • Collaborative, agile, and diverse engineering team environment
Skills & Technologies
See exactly how your profile matches this role — strengths, skill gaps, and what to do about them.
About Serverobotics Inc.
Serverobotics develops cloud robotics software that lets users monitor, control, and manage fleets of internet-connected robots from any web browser. The platform integrates real-time telemetry, remote tele-operation, over-the-air updates, and secure data logging, enabling teams to deploy, debug, and scale robotic systems without on-site personnel. Core products include a web dashboard, REST and WebSocket APIs, and edge device agents that run on Linux-based robots. Industries served range from service and industrial automation to research and education, with emphasis on reducing operational downtime and accelerating development cycles through centralized fleet management.
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